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Creators/Authors contains: "Purviance, John ."

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  1. ; ; ; ; ; (, Proceedings of the ACM on Human-Computer Interaction)
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  2. ; ; (, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2018))
    Many types of investigative work involve verifying the legitimacy of visual evidence by identifying the precise geographic location where a photo or video was taken. Professional geolocation is often a manual, time-consuming process that can involve searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds so that they can support an expert's search. In an experiment (n=540), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision. 
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  3. ; ; (, AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2017))
    Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, time-consuming process that involves searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds, allowing them to support an expert’s search. In two experiments (n=1080), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision. 
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    Accepted Manuscript Full Text Available